期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2019
卷号：15
期号：11
页码：1
DOI：10.1177/1550147719888164
出版社：Hindawi Publishing Corporation
摘要：Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.
关键词：Moving objects; activity pattern discovery; trajectory clustering; spatial–temporal data mining
